Simplifying Bayesian Inference: The General Case
Stefan Krauss (),
Laura Martignon and
Ulrich Hoffrage ()
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Stefan Krauss: MPI for Human Development, Postal: Lentzeallee 94, 14195 Berlin
Laura Martignon: Max Planck Institute for Human Development, Postal: Lentzeallee 94, D-14195 Berlin
Ulrich Hoffrage: Sonderforschungsbereich 504, Postal: L 13, 15, D-68131 Mannheim
No 99-23, Sonderforschungsbereich 504 Publications from Sonderforschungsbereich 504, Universität Mannheim, Sonderforschungsbereich 504, University of Mannheim
We present empirical evidence that human reasoning follows the rules of probability theory, if information is presented in änatural formats¶. Human reasoning has often been evaluated in terms of humansÁ ability to deal with probabilities. Yet, in nature we do not observe probabilities, we rather count samples and their subsets. Our concept of Markov frequencies generalizes Gigerenzer & Hoffrageãs änatural frequencies, which are known to foster insight in Bayesian situations with one cue. Markov Frequencies allow to visualize Bayesian inference problems even with an arbitrary number of cues.
Pages: 14 pages
Note: Financial Support from the Deutsche Forschungsgemeinschaft, SFB 504, at the University of Mannheim, is gratefully acknowledged.
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